Systems, devices and methods for the quality assessment of OLED stack films

ABSTRACT

This disclosure provides techniques for assessing quality of a deposited film layer of an organic light emitting diode (“OLED”) device. An image is captured and filtered to identify a deposited layer that is to be analyzed. Image data representing this layer can be optionally converted to brightness (grayscale) data. A gradient function is then applied to emphasize discontinuities in the deposited layer. Discontinuities are then compared to one or more thresholds and used to ascertain quality of the deposited layer, with optional remedial measures then being applied. The disclosed techniques can be applied in situ, to quickly identify potential defects such as delamination before ensuing manufacturing steps are applied. In optional embodiments, remedial measures can be taken dependent on whether defects are determined to exist.

This application is a continuation application of U.S. Utilityapplication Ser. No. 15/709,320 filed on Sep. 19, 2017. U.S. Utilityapplication Ser. No. 15/709,320 is a continuation application of U.S.Utility application Ser. No. 15/250,283, filed on Aug. 29, 2016. U.S.Utility application Ser. No. 15/250,283 in turn is a continuation ofU.S. application Ser. No. 14/180,015, filed on Feb. 13, 2014 (issued onSep. 13, 2016 as U.S. Pat. No. 9,443,299). U.S. Utility application Ser.No. 14/180,015 claims benefit of U.S. Provisional Patent Application No.61/766,064, filed on Feb. 18, 2013. All applications named in thissection are hereby incorporated by reference.

FIELD

The present teachings are related to systems, devices and methods usefulfor quality assessment of various films formed in pixel well structuresduring the manufacture of an organic light emitting diode (“OLED”)device.

BACKGROUND

Interest in the potential of OLED device technology has been driven inlarge part by the demonstration of flat panels that havehighly-saturated colors and have high contrast, and that are ultrathinand energy efficient. Additionally, a wide variety of substratematerials, including flexible polymeric materials, can be used in thefabrication of OLED devices. An OLED device can be manufactured by theprinting of various organic and other thin films onto a substrate usingan industrial printing system. Nearly any desired size of substrate canbe used in such a process, from substrates sized for use as cell phonedisplays to substrates sized for use as very large television (“TV”)screens. To provide two non-limiting example, ink jet printing of thinfilms can be used for Gen 7.5 substrates, having dimensions of about 195cm×225 cm, with these substrates then being cut into eight 42″ or six47″ flat panels per substrate, and for Gen 8.5 substrates, havingdimensions of about 220×250 cm, with these substrates then being cutinto six 55″ or eight 46″ flat panels per substrate.

OLED devices typically have a number of pixels that make up a display.In a color display, each pixel typically has three separate colorgenerating elements. Each of these elements, in turn, typically uses a“well” to receive one or more thin film layers during an ink jetprinting process. Thus, each pixel of the OLED device is typicallyassociated with three wells corresponding to respective pixel colors.The assemblage of layers for each color component (i.e., associated witheach well) forms an “OLED stack.” Each OLED stack can include 6-7 filmlayers. During manufacture, it is desired to uniformly deposit each ofthese layers.

For perspective, a high-definition flat panel display can contain over 2million pixels with a pixel density of between about 300 ppi to about450 ppi. Clearly, given the sheer number of functioning pixels that mustbe formed on a substrate during manufacture of various OLED devices, ahigh degree of manufacturing accuracy is required. In the process offorming the various layers, various discontinuities between or withinfilm layers can occur, which can result in a pixel that does not performas designed or is otherwise identified as defective.

Accordingly, there is a need in the art for systems, devices and methodsthat can be used to timely and systematically assess quality of thinfilms formed on a substrate during the manufacture of an OLED device.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative embodiments of the invention will hereafter be describedwith reference to the accompanying drawings.

FIG. 1A is a schematic representation of an exemplary pixel arrangementwithin a display panel in accordance with the present teachings. FIG. 1Bis a schematic depiction of an embodiment of an OLED stack in accordancewith the present teachings.

FIG. 2A is a section view that depicts an illustrative pixel well inaccordance with the present teachings. FIG. 2B is a top view thatdepicts a structure associated with a single pixel in accordance withthe present teachings.

FIG. 3 depicts a schematic representation of a panel inspection systemin accordance with an illustrative embodiment.

FIG. 4 depicts a block diagram of a data collection apparatus of thepanel inspection system of FIG. 3 in accordance with an illustrativeembodiment.

FIG. 5 depicts a printing system with a data collection assembly of FIG.4 in accordance with an illustrative embodiment.

FIG. 6 is a schematic section view of a gas enclosure system that canhouse a printing system, such as various embodiments of a printingsystem of FIG. 5.

FIG. 7A through FIG. 7D depict various flow diagrams illustratingexample operations performed by an image processing application of adata collection apparatus in accordance with various embodiments ofsystems and methods of the present teachings.

FIG. 8A through FIG. 8F depict schematic representations of a one ormore pixels wells, used to illustrate operation of an image processingapplication in accordance with various embodiments of the presentteachings.

FIG. 9 illustrates a histogram of gradient intensities, corresponding toa representation of a deposited layer within a pixel well, seen to theleft-side of FIG. 9.

FIG. 10 illustrates a histogram of gradient intensities, correspondingto a representation of a deposited layer within a pixel well, seen tothe left-side of FIG. 10.

FIG. 11 depicts a flow diagram illustrating example operations performedby an image processing application of a data collection apparatus inaccordance with various embodiments of the present teachings.

FIG. 12 depicts a flow diagram illustrating example operations performedby an image processing application of a data collection apparatus inaccordance with various embodiments of the present teachings.

DETAILED DESCRIPTION

This disclosure provides systems, devices and methods for the evaluationof quality of a thin film layer deposited during OLED devicefabrication. One or more layers of an OLED stack can be successivelyprinted onto target areas of a substrate; each target area is optionallya pixel well that will be associated with a specific color component ofa pixel of light to be generated by the finished OLED device. An inkjetprinting process is optionally used for this printing process. Depositedlayers can be formed of either organic or inorganic materials, buttypically, the OLED stack includes at least one organic layer formedusing this process (e.g., optionally an emissive material layer or“EML”). After printing a specific ink into targeted areas, one or morepost-printing processing steps can be performed in order to finish eachlayer, for example, by converting deposited fluid into a permanentstructure. In order to assess the quality of each layer formed in eachtarget area, images of all target areas are captured during or followinglayer deposition and/or formation, for example, using a high-speed,high-resolution camera. Such imaging can optionally be performed priorto the deposition of an ensuing layer of the OLED stack, in order toassess quality of a previous ‘wet’ layer or finished layer (i.e., at anystage of the layer formation process).

Non-uniformity in a deposited layer can be detected through theevaluation of image data taken as a result of this image capture. Eachcaptured image is typically a high-resolution close-up view of one ormore pixel wells or one or more pixels of an OLED device substrate.Non-uniformity can be expressed as a discontinuity between film layerswithin a pixel well, for example, which indicates a delamination betweenfilms, gaps, pinholes or other types of issues. Each captured image canencompass, by way of non-limiting example, one or more pixel wells, areasurrounding the one or more pixel wells, a bank that defines confines ofeach pixel well, and a film deposited within each pixel well to formpart of an OLED stack. A captured image can be filtered to isolategenerate filtered data to isolate image data corresponding to just thedeposited layer of interest film, and to remove superfluous data (suchas image data for areas outside of a given pixel well or wells). Thisfiltered data is typically image data just of the film layer inquestion. A gradient function can be applied to this filtered data toform processed data. The processed data is typically an image ofgradient values that highlights discontinuities from uniform image data.For various embodiments of systems and methods of the present teachings,such processed data can be used to evaluate the quality of an OLED stackfilm in one or more wells, for example, dependent on the magnitude ofdiscontinuities, the number of discontinuities, or one or more othercriteria. A result or output can then be generated representing thequality of a pixel well that has been evaluated using the processedimage data. In various embodiments of the present teachings, this outputcan represent whether or not a deposited layer within a particular pixelwell has a fill issue or a delamination issue. In another embodiment,the described process can be iteratively applied for each layer in anOLED stack within a pixel well, with a result indicating an unacceptableerror and consequently being used to dictate subsequent processing.Finally, remedial measures can then optionally be taken if any defectsare identified. As should be apparent, no manufacturing process isperfect, and the timely detection of defects and use of such remedialmeasures is important to maximizing quality and production speed andminimizing cost.

In one embodiment, the filtered data (i.e., image data representing justthe deposited layer under analysis) can be converted (prior toapplication of the gradient function) to a specific format that helpsemphasize a specific image characteristic, such as brightness, grayscale value, hue, color intensity, or another image characteristic. Inone embodiment, for example, filtered data representing a color image isconverted to 8-bit grayscale intensity values, one for each pixel or“PEL” of the filtered data (“PEL will typically be used in referring topixels of high-resolution captured data from the camera, whereas “pixel”will typically be used in referring to picture elements of a finishedOLED panel and associated light generating components and/or the areasoccupied by these components). This emphasized data (e.g., monochromaticimage data in the case of grayscale conversion) is then subjected to thegradient function to produce the processed data. The gradient functionaccentuates non-uniformity on a localized basis within a pixel wellunder scrutiny, as mentioned.

One embodiment of the present teachings provides a computer-readablemedium having stored thereon computer-readable instructions. Whenexecuted, these computer-readable instructions cause a processor toprocess captured imaged data representing a target area to analyzequality of a deposited film. Once again, the target area can include atleast one pixel well represented by a captured image. Thecomputer-readable medium is a non-transitory medium, meaning that it isa physical structure adapted to store data in electronic, magnetic,optical or other form. Examples of such a medium include, for example, aflash drive, floppy disk, tape, server storage or mass storage, harddrive, dynamic random access memory (DRAM), compact disk (CD) or otherlocal or remote storage. The computer-readable medium can haveinstructions stored thereon that when executed by the processor cancause a system to perform various embodiments of a method foridentifying discontinuities in a filmed formed on an OLED devicesubstrate. The computer-readable medium can be optionally embodied aspart of a larger machine or system (e.g., resident memory in a desktopcomputer or printer) or it can exist on an isolated basis (e.g., flashdrive or standalone storage that will later transfer a file to anothercomputer or printer). The larger machine or system can optionallyinclude a camera and a processor and, optionally, a printing deviceincluding a printhead and/or camera conveyance mechanism. The camera canbe mounted on an assembly that includes a light source for illuminatinga target area of an OLED device substrate during image capture, i.e., totake a picture of an inactive OLED device substrate during fabrication,using illumination external to that substrate.

In still more detailed embodiments, the gradient function is applied bya mathematical process against image data representing a specific targetarea, as a convolution between elements from two matrices. This imagedata can be the filtered data referred to above, and typically includesimage data derived from PELS of the captured image. This image data asmentioned can emphasize a specific image characteristic, such as colorintensity, brightness, and so forth. The convolution can optionally beperformed by software or firmware, that is, by one or more machinesacting under the control of software and/or firmware. The result of theconvolution can be expressed as a matrix of gradient valuescorresponding to a film layer under scrutiny. A measure is then applied(i.e., one or more criteria or thresholds) to assess quality of thedeposited film. If the result meets the applied measure(s), thedeposited layer is determined to have satisfactory quality, andotherwise is determined to present a possible issue. As a non-limitingexample of an action that can be taken in response to identification ofan issue, the display panel can be rejected without further processing(potentially saving manufacturing time and expense) or subjected tofurther processing in an effort to remedy the issue. Note that a widevariety of different criteria can be applied to assess quality; forexample, in one specific detailed embodiment discussed below, theabsolute values of gradient values for a layer within a pixel well aresummed together (or the squares of the gradient values are summedtogether), and the deposited film is determined to be acceptable if theresultant sum is less than a threshold. In another embodiment, a secondtest can instead be applied to distinguish a large noticeablediscontinuity or gradient (e.g., a significant problem) from a number ofsmall discontinuities (e.g., which might represent no problem); in thisregard a statistical test can also be applied (e.g., comparison of astandard deviation of values from the result matrix with a secondthreshold) to identify any particular characteristic that mightrepresent an issue. For example, in one application discussed below, ahistogram is computed for the gradient values associated with each pixelwell containing the layer under scrutiny; a high incidence of intensegradients might indicate a “racetrack” effect associated withdelamination within a given pixel well. These tests may also be used incombination with one another if desired, for example, to deem a layerwithin a given well as of acceptable quality only if both tests aresatisfied. Clearly, many alternate tests and combinations of tests alsoexist and can be selected for various embodiments.

In still another embodiment, a second gradient function can optionallybe applied. For example, the filtering process discussed above can alsouse a gradient function to identify contours of the deposited layer fromthe captured image, with these contours then being used to mask outportions of the captured image not directly representing the depositedlayer under consideration. In a more specific implementation, a copy ofeach captured image is filtered using such a gradient function to locate“banks” that structurally define contours of each pixel well. A maskfunction is then defined based on this analysis to pass (i.e., not mask)image data within the banks (for example, that might correspond to thedeposited layer of interest) and to block (i.e., mask out) image datacorresponding to substrate structures outside of the confines of thepixel well at issue (i.e., lying outside of the banks). This mask isthen applied to the original image data in order to obtain the filtereddata (i.e., representing just the layer under scrutiny).

As should be apparent from the foregoing, the various operationsdiscussed above, and methods, devices and systems that perform thoseoperations, facilitate timely and systematic evaluation of the qualityof films of an OLED device. These various methods, devices and systemstherefore lead to more reliable OLED devices, because potential defectscan be more accurately detected; they also lead to reduced manufacturingcosts, because such defects can be detected (and potentially cured)without the expense and time associated with further processing on apotentially defective OLED device, or expense and time associated with adiscard of salvageable materials.

Additional details and options will be evident to those skilled in theart from the discussion below.

FIG. 1A is a schematic top view of an OLED device substrate 10, with anexpanded view 20 showing circuitry for six pixels formed on the surfaceof substrate 10. A single pixel is designated by numeral 30, and is seento consist of separate red, green and blue light generating elements(32, 34 and 36). Additional circuitry (such as depicted by numeral 38)can be formed on the OLED device display substrate to assist withcontrol over generation of light by a respective pixel well.

As previously mentioned, during the manufacture of an OLED flat paneldisplay, an OLED pixel is formed to include at least one OLED filmstack, which can emit light when a voltage is applied. FIG. 1B depictsan embodiment of an OLED stack film structure that includes between theanode and the cathode a hole injection layer (HIL), a hole transportlayer (HTL), an emissive layer (EML), and an electron transport layer(ETL) combined with an electron injection layer (EIL). When voltage isapplied across the anode and cathode, light of a specific wavelength isemitted from the EML layer, as indicated in FIG. 1B. In variousembodiments of systems, devices and methods of the present teachings,the HIL, HTL, EML, and ETL/EIL layers depicted in FIG. 1B can be printedusing inkjet printing. Each of the HIL, HTL, and ETL.EIL OLED stacklayers has an ink formulation including materials that define thefunction of those OLED stack film layers. As will be discussed in moredetail subsequently, a pixel can include three color generatingelements, where each element has an EML layer that emits a differentwavelength of light, for example, but not limited by, red, green andblue. For various embodiments of an OLED pixel cell of the presentteachings, each EML layer has an ink formulation including an OLEDmaterial that can emit in the targeted electromagnetic wavelength range.Note that it is possible to have a monochromatic display (e.g., with asingle pixel well for each pixel), as well as any number or combinationof color components and associated pixel wells.

FIG. 2A shows a cross-section of a pixel well 50, into which inks forforming various OLED stack layers can be printed to define a singlecolor generating element. While the cross-section depicts a pair ofdouble bank structures (designated by numerals 52 and 54) that serve toconfine deposited fluids, in fact the these structures typically formpart of a single, closed shape (e.g., such as represented by thecontours of red, green or blue light generation areas, respectively,numerals 62, 64 and 66 of FIG. 2B). In the case of pixel well 50, thesubstrate 51 is typically a transparent structure and, consequently,region 58 represents an area of active pixels width that will beassociated with (unimpeded) generated light, whereas area 56 representsa well width associated with deposited layers. Relative heights anddistances of the various structures are identified by a legend seen atthe upper-right-hand side of FIG. 2A. As referenced earlier, in someembodiments, the pixel bank, more specifically confinement bank 54, isdetected and used to filter the captured image data that is used foranalyzing a deposited layer, such that only this filtered image datacorresponding to the pixel well width (56) is processed to asses layerdiscontinuities. This is not required for all embodiments.

FIG. 2B depicts a plan view of pixel cell 60, having dimensions ofbetween about 95 width by about 85 in length. In pixel cell 60, thereare three different wells (62, 64 and 66) that are each used to receivelayers that will define an OLED stack, one stack for a red lightgenerating component (62), one stack for a green light generatingcomponent (64), and one stack for a blue light generating component(66). Dimensions identified within FIG. 2B identify pixel cell pitch andpixel well dimensions for each color component for a flat panel display(having a resolution of 326 pixels per inch or “ppi”).

As one of ordinary skill in the art can readily appreciate, pixel size,shape, and aspect ratios can vary depending on a number of factors. Forexample, a pixel density corresponding to 100 ppi can be sufficient fora panel used for a computer display. By contrast, a pixel densitycorresponding to 300 ppi to about 450 ppi can be used for very highresolution displays. Note that a wide variety of inks can be used toform different layers for use with light of various wavelengths, suchthat each of wells 62, 64 and 66 do not have to use the same ink orinks. Moreover, a variety of inks can be formulated to form other layersas depicted in FIG. 1B.

The present teachings facilitate automated systems and methods for thetimely inspection and objective determination of quality of a depositedlayer over a large number of pixel wells of an OLED device substrate.For example, reflecting on the discussion of different substrate sizespresented earlier, it will be recalled that for many designs (e.g., Gen5.5 to Gen 8.5), there typically are millions of pixels per panel. Thisnumber may increase dramatically in the future as additional displaytechnologies come to fruition. As previously discussed, for properfunction in an OLED device, film uniformity is desirable.

FIG. 3 is a schematic depiction of an OLED device quality assessmentsystem 300 in accordance with an illustrative embodiment. Qualityassessment system 300 can include a data collection system 304, an imageprocessing system 306, and a network 308, as well as a data storagesystem 305. The data collection system 304 includes a camera, forexample, a high speed CCD camera. The camera captures imagesrepresenting high-magnification, high-resolution views of the OLEDdevice substrate. The data collection system 304 can also optionally beimplemented with an OLED device fabrication mechanism, including one ormore printers and/or other devices. In one embodiment, the camera iscarried by a transport mechanism used to position the camera as well asto position ink jet printheads used to print the various film layers.Captured images and/or other data can be stored in the storage system305. The image processing system 306 processes the captured images anddetermines whether or not a pixel is defective or otherwise might notperform as designed for the application. Typically, the image processingsystem includes computer readable media (i.e. part of the depictedportable device, desktop computer or laptop or other processing device)that causes one or more processors of the image processing system 306 toperform this analysis. The network 308 that connects these variouselements can include one or more networks of the same or differenttypes. The network 308 can include any type of wired and wireless publicor private network including but not limited to a cellular network, aBluetooth network, a peer-to-peer network, a local area network, a widearea network such as the Internet, an internal system bus, or anotherscheme for connecting the various depicted devices. The network 308 canfurther include sub-networks and consist of any number of additional orfewer devices. The components of OLED device quality inspection system300 can be included in a single computing device, can be positioned in asingle room or adjacent rooms, in a single facility, or can be remotefrom one another. In various embodiments, the present teachings can beembodied in any single component illustrated in FIG. 3 or as acollection of such components, or as related methods, devices orsystems.

FIG. 4 shows a block diagram of a data collection apparatus 400 inaccordance with an illustrative embodiment. The data collectionapparatus 400 can include, to provide one non-limiting example, acollection assembly 450, a camera assembly 460, a positioning system 470and at least one processor 406. To assist with input and output of datato the at least one processor 406, the data collection apparatus 400 canfurther include an input interface 402, an output interface 404, acommunication interface 408, a keyboard 410, a mouse 412, a display 414,a printer 416, computer-readable medium 420 and a data collectionapplication 430. Fewer, different, and additional components can beincorporated into the data collection apparatus 400.

With respect to collection assembly 450 of data collection apparatus 400of FIG. 4, camera assembly 460 can optionally be configured to captureimage data using a lens, as understood by a person of skill in the art.The captured image data is received into data collection apparatus 400through input interface 402. The data collection assembly 450 can alsoinclude the positioning system 470, which is used to mount the cameraassembly 460 and to transport the camera assembly relative to an OLEDdevice substrate. In this regard, the positioning system 470 providesfor travel of camera assembly 460 over the entire surface of a flatpanel display. Accordingly, data collection assembly 450 of collectionapparatus 400 can fully capture images representing an entire set ofpixel wells of any flat panel display device. Note that as mentionedearlier, in one embodiment, the camera assembly 460 can be part of aprinter (e.g., printer 416), with the processor 406 and/or collectionassembly 450 also used to control print head motion. In such a system,the camera assembly 460 can be optionally integrated with a print head,so as to capture images of just-deposited “wet inks” for purposes ofquality analysis. Alternatively, the camera assembly 460 can beoptionally implemented as a part of a multi-tool printer where thepositioning system 470 serves double-duty, at some times mounting one ormore printheads for use in printing an OLED device substrate, and atother times mounting the camera assembly 460. In one embodiment, amanufacturing apparatus for OLED devices pipelines multiple substrates,such that after a layer is deposited over a first substrate in aprinter, that substrate is advanced to another position within themanufacturing apparatus and is imaged for quality analysis;simultaneously, a second substrate is brought into the printer forpurposes of depositing a similar layer.

Input interface 402 provides a port to receive data into data collectionapparatus 400, as understood by those skilled in the art. The inputinterface 402 can interface with various input technologies including,but not limited to, keyboard 410, mouse 412, display 414, cameraassembly 460, a track ball, a keypad, one or more buttons, etc., toallow information to be received into data collection apparatus 400 orto make selections from a user interface displayed on display 414.Keyboard 410 can be any of a variety of keyboards as understood by thoseskilled in the art. Display 414 can be a thin film transistor display, alight emitting diode display, a liquid crystal display, or any of avariety of different displays as understood by those skilled in the art.Mouse 412 can be any of a variety of mouse-type devices as understood bythose skilled in the art. The same interface can support both inputinterface 402 and output interface 404. For example, display 414 caninclude a touch screen that supports user input and presents output tothe user. Data collection apparatus 400 can have one or more inputinterfaces that use the same or a different input interface technology.Keyboard 410, mouse 412, display 414, camera assembly 460, etc., furthercan be accessible by data collection apparatus 400 through communicationinterface 408.

The output interface 404 can provide an interface for outputtinginformation from data collection apparatus 400. For example, outputinterface 404 can interface with various output technologies including,but not limited to, a display, a printer, a camera, a positioningsystem, or another device if not part of the data collection apparatus400. In at least one embodiment, the output interface 404 comprises alocal area connection or a wireless connection for purposes ofcommunicating with other network-connected elements.

The processor 406 executes instructions as understood by those skilledin the art. The instructions can be carried out by a special purposecomputer, logic circuits, or hardware circuits. The term “execution”refers to the process of performing operations called for by respectiveinstructions. The instructions can be expressed in the form of one ormore programming languages, scripting languages, assembly languages,etc. The processor 406 receives instructions from a non-transitorycomputer-readable medium, for example, a local, network-attached orremote memory, and executes such instructions, for example by storing acopy of these instructions in local RAM and then acting on thoselocally-stored instructions. The data collection apparatus can alsoinclude a number of processors to perform the various functions, forexample, implemented as a multi-core device, as a set of processors ofan integrated device, or as a set of network-attached devices thatcollectively perform operations and that exchange data in associationwith those operations.

FIG. 5 depicts a front perspective view of an inkjet printing system 500for printing various OLED stack layers on an OLED substrate, inaccordance with an illustrative embodiment. An OLED inkjet printingsystem, such as inkjet printing system 500 of FIG. 5, can be comprisedof several devices and apparatuses, which allow the reliable placementof ink drops onto specific locations on a substrate, such as substrate522 positioned upon substrate support apparatus 520. These devices andapparatuses can include, without limitation, a printhead assembly, inkdelivery system, motion system, substrate support apparatus, and asubstrate loading and unloading system. A motion system can be, by wayof non-limiting example, a gantry system or split axis XYZ system, asdepicted for inkjet printing system 500 of FIG. 5. Either the printheadassembly can move over a stationary substrate (gantry style), or boththe printhead and substrate can move (in the case of a split axisconfiguration). In another embodiment, a printhead assembly can besubstantially stationary; for example, the substrate can be transportedin X and Y dimensions relative to one or more printheads, with Z axismotion provided either by a substrate support apparatus or by a Z-axismotion system associated with the printhead assembly. As the one or moreprintheads move relative to the substrate, droplets of ink are ejectedat the correct time to be deposited in the desired location on asubstrate. A substrate can be inserted and removed from the printerusing a substrate loading and unloading system. Depending on theconfiguration of an inkjet printing system, this can be accomplishedusing a mechanical conveyor, a substrate floatation table with aconveyance assembly, or a substrate transfer robot with end effector.Note that in one embodiment, printing is performed inside a chamber thatis sealed relative to ambient air, to exclude unwanted particulate. Thedata collection system can also optionally be operated in this isolatedenvironment. In one contemplated application, referenced below, thesealed chamber can be used to perform printing and/or imaging in thepresence of a specifically selected atmosphere, such as an inert gas.

The inkjet printing system 500 of FIG. 5 can include a data collectionassembly, as has been described previously. Various embodiments ofinkjet printing system 500 can be supported by a printing system base510. First and second risers 512 and 514 above this base support abridge 516, used to support the printing system and the imaging system(e.g., the camera). A cable tray assembly 542 provides enclosure androuting for various cable, wire, and tubing assemblies required for theoperation of inkjet printing system 500, and helps contain any particlesgenerated by such cable, wire, and tubing assemblies. The cable trayassembly 542 is generally part of a cable tray assembly exhaust system540, which provides for exhaust of such particles via tray exhaustplenum 544. In this regard, the cable tray exhaust assembly 540 can helpfacilitate a low-particle environment during the printing and/or imagingprocess.

The bridge 516 helps support a first X,Z-axis carriage assembly 530 andsecond X,Z-axis carriage assembly 530A. Each carriage assembly can movein an X-axis direction on bridge 516 relative to substrate supportapparatus 520. The inkjet printing system 500 can use an intrinsicallylow-particle generating X-axis motion system, in which first and secondX,Z carriage assemblies 530 and 530A, respectively, move on an airbearing linear slider assembly. As one of ordinary skill in the art mayunderstand, an air bearing linear slider assembly can wrap around theentirety of bridge 516, allowing frictionless movement of first andsecond X,Z carriage assembly 530 and 530A. Such an air bearing linearslider assembly helps provide a three-point mounting, which preservesaccuracy of travel for each X,Z carriage assembly and helps resist skew.The first X,Z-axis carriage assembly 530 has a first Z-axis moving plate532, while the second X,Z-axis carriage assembly 530 has a second Z-axismoving plate 532A; each Z-axis moving plate helps control Z-axismovement of an apparatus mounting thereon. For inkjet printing system500, printhead assembly 550 is depicted mounted on Z-axis moving plate532, while camera assembly 560 is depicted mounted on Z-axis movingplate 532A. It can be readily appreciated that in various embodiments ofan inkjet printing system having two X,Z-axis carriage assemblies, aprinthead assembly and a camera assembly can be mounted interchangeablyon either X,Z-axis carriage assembly. Other embodiments can use a singleX,Z-axis carriage assembly having a printhead assembly mounted thereon,and a camera assembly mounted proximate to the printhead assembly.

In various embodiments of inkjet printing system 500, a substratesupport apparatus 520 can be a floatation table that provides forfrictionless support of substrate 522; in other embodiments, thesubstrate support apparatus 520 can be a chuck. The Y-axis motion system524, depicted in FIG. 5 as a unitary rail system, can utilize a linearair bearing motion system or a linear mechanical bearing motion system;the air bearing motion system helps facilitation frictionless conveyanceof substrate 520 in the Y-axis direction. The Y-axis motion system 524can also optionally use dual rail motion, once again, provided by alinear air bearing motion system or a linear mechanical bearing motionsystem.

A camera assembly 560 as depicted in FIG. 5 can include a camera 562, acamera mount assembly 564 and a lens assembly 568. The lens assembly canbe mounted to the Z-axis moving plate 532A via the camera mount assembly564. The camera 562 can be any image sensor device that converts anoptical image into an electronic signal, such as by way of non-limitingexample, a charge-coupled device (CCD), a complementarymetal-oxide-semiconductor (CMOS) device or N-typemetal-oxide-semiconductor (NMOS) device. As depicted in FIG. 5, theZ-axis moving plate 532A can controllably adjust the Z-axis position ofcamera assembly 560 relative to substrate 522. During various processes,such as for example, printing and data collection, substrate 522 can becontrollably positioned relative to the camera assembly 560 using theY-axis motion system 524. The camera assembly 560 as depicted in FIG. 5can also be controllably positioned relative to substrate 522 via X-axismovement via carriage assembly 530B mounted on the bridge. Accordingly,the split axis motion system of FIG. 5 can provide precise positioningof the camera assembly 560 and substrate 522 relative to one another inthree dimensions in order to capture image data on any part of thesubstrate 522 at any desired focus and/or height. As previouslymentioned, other motion systems, such as a gantry motion system, canalso be used to provide precision movement in three dimensions between,for example, a printhead assembly and/or a camera assembly, relative toa substrate. In either embodiment, a motion system can position cameraassembly 560 relative to substrate 522 using a continuous or a steppedmotion or a combination thereof to capture a series of one or moreimages of the surface of substrate 522. Each image can encompass an areaassociated with one or more pixel wells, individual pixels, or anycombination thereof and including the surrounding surface area (e.g.,encompassing for example associated electronic circuitry components,pathways and connectors). Any of a variety of cameras can be used thatcan have a relatively large field of view (>0.5 mm) while providingfairly fine granularity (3 micron per pixel resolution).

FIG. 6 is a schematic representation of a gas enclosure system 600 thatcan house a printing system, as referenced earlier. The gas enclosuresystem 600 can comprise a gas enclosure assembly 650, a gas purificationloop 630 in fluid communication with gas enclosure assembly 650, and atleast one thermal regulation system 640. Additionally, variousembodiments of a gas enclosure system can have pressurized inert gasrecirculation system 660, which can supply inert gas for operatingvarious devices of an OLED printing system, such as a substratefloatation table, an air bearing linear slider assembly, and a linearair bearing assembly. Various embodiments of a pressurized inert gasrecirculation system 660 can utilize a compressor, a blower andcombinations of the two as sources for various embodiments of inert gasrecirculation system 660. Additionally, gas enclosure system 600 canhave a filtration and circulation system (not shown) internal to gasenclosure system 600, which along with other components, such as a cabletray exhaust assembly, a substrate floatation table, an air bearinglinear slider assembly, and a linear air bearing assembly, can provide asubstantially low-particle printing environment.

As depicted in FIG. 6, for various embodiments of gas enclosure assembly600, a gas purification loop 630 can include an outlet line 631 from gasenclosure assembly 650 to a solvent removal component 632, with ensuingcoupling to a gas purification system 634. Inert gas purified of solventand other reactive gas species, such as oxygen and water vapor, are thenreturned to gas enclosure assembly 650 through inlet line 633. The gaspurification loop 630 can also include appropriate conduits andconnections, and sensors, for example, oxygen, water vapor and solventvapor sensors. A gas circulating unit, such as a fan, blower or motorand the like, can be separately provided or integrated (e.g., in gaspurification system 634) to circulate gas through gas purification loop630. Though the solvent removal system 632 and the gas purificationsystem 634 are shown as separate units in FIG. 6, the solvent removalsystem 632 and the gas purification system 634 can also be housedtogether as a single purification unit. Each thermal regulation system640 can include, for example, at least one chiller 641 (e.g., which canhave fluid outlet line 643 for circulating a coolant into a gasenclosure assembly) and a fluid inlet line 645 for returning the coolantto the chiller.

For various embodiments of gas enclosure assembly 600, a gas source canbe an inert gas, such as nitrogen, any of the noble gases, and anycombination thereof. For various embodiments of gas enclosure assembly600, a gas source can be a source of a gas such as clean dry air (CDA).For various embodiments of gas enclosure assembly 600, a gas source canbe a source supplying a combination of an inert gas and a gas such asCDA.

The system 600 also helps maintain levels for various reactive gasspecies as needed, including gases such as water vapor, oxygen and/ororganic solvent vapors at 100 ppm or lower (for example, at 10 ppm orlower, at 1.0 ppm or lower, or at 0.1 ppm or lower). Further, the gasenclosure system 600 helps provide a low particle environment meeting arange of specifications for airborne particulate matter according to ISO14644 Class 1 through Class 5 clean room standards.

Note that as referenced earlier, the gas enclosure system 600 supportsprinting and/or imaging. For example, the gas enclosure system can use asubstrate floatation table, an air bearing linear slider assembly,and/or a linear air bearing assembly, to move a substrate into and outof a printer. The gas enclosure system can also optionally enclose adata collection apparatus (e.g., such as a camera, camera positioningsystems and other image capture elements), as discussed above.

FIG. 7A shows a flow chart 701 associated with one method for processingimage data. This method can optionally be implemented as instructionsstored on computer-readable media, i.e., as software or firmware whichcontrols one or more processors as introduced earlier. As depicted bynumeral 703, captured image data is first received by the processor.Such data could have been captured under the control of the processor,or it could be retrieved from a file stored locally or remotely; in oneembodiment, the captured image data is received directly from the cameraat the time it captures each image as a “stream” of captured images asthe camera is continuously or stepwise scanned over various pixel wells.Captured image data is then filtered (705) to isolate a deposited film(or films) of interest. For filtered data produced as a result of thisprocess, for example, representing image data associated with theinteriors of one or more pixel wells, film integrity is then analyzed(707). Note that it was earlier-mentioned that in one embodiment, thisanalysis is performed upon a finished layer following any post-printingfinishing steps. In another embodiment, this analysis (and imagecapture) can be performed on still-wet ink, for example, to identifysituations where a fill is incomplete, where an air bubble exists withinthe ink, or another potential defect. In various embodiments, remedialefforts (708) can be taken in response to a detected defect. Forexample, the OLED device substrate can be discarded (thereby potentiallysaving additional time and deposition expense), or alternatively, adefect can be fixed by removing the deposited layer, by adding to thedeposited layer, or by further processing the deposited layer. If theanalysis is performed on a wet ink for example, the wet ink can beremoved and deposition reperformed; if an air bubble exists, the ink canbe heated or otherwise processed to eliminate the air bubble; if a fillis incomplete, additional ink can be added at precise locations tocomplete the fill. Many other possible remedial measures exist,providing for a substantially more efficient manufacturing process.

If no defect is identified, processing then continues to the next pixelwell (709). Note that this function is depicted in dashed-lines toindicate its optional nature. In an embodiment that scrutinizes layersin multiple wells at-once, a given captured image may represent morethan one pixel well and potentially more than one deposited layer or inkthat requires analysis. Numeral 709 indicates that should this be thecase, the captured image received in step 703 can be further processedfor such additional pixel wells, if any. In one embodiment, eachcaptured image encompasses an area associated with one or more pixels,in a second embodiment, each captured image encompasses (or is filteredto identify) only a single pixel well, and in a third embodiment,multiple pixel wells representing one or more pixels or a subset of oneor more pixels can be processed at-once. If a given captured image hasbeen fully processed, or if additional pixel wells still requireanalysis for a given deposition, the method then loops to consider adifferent captured image, as represented by numerals 711 and 713. If allpixel wells have been evaluated and pass desired quality thresholds,i.e., no defects are detected, the analysis then completes, as indicatedby reference numeral 715.

FIG. 7B is used to provide additional detail on an exemplary filteringoperation, used to isolate image data corresponding to just a depositedlayer under scrutiny. This filtering operation is generally identifiedby a series of steps 721. As indicated by numeral 723, image datacorresponding to a captured image is received and stored in fast-accessmemory, such as RAM. The image can be received on the fly, or can beretrieved from machine-readable media, as mentioned. Note that FIG. 8Aprovides an example of one hypothetical image, showing about two and onehalf pixel wells in a single view. In one embodiment, processing focuseson one of these pixel wells at a time, while in another embodiment,multiple pixel wells (such as wells 807 and 809) can be consideredsimultaneously. A boundary detection function (725) is then applied toisolate expected metes and bounds of the deposited layer under scrutiny.Optionally, such a detection function can also be applied using agradient function, i.e., which enables identification of a boundary orcontour (727) corresponding to layer periphery. Such a contour forexample can be compared to expected data (729) to determine adequacy offill. For example, looking ahead to FIGS. 8A-8F, the existence of anarea of incomplete fill (832) can be detected by comparing data for areference pixel well (FIG. 8C) with filtered image data for a depositedlayer (FIG. 8D) to ascertain certain types of deposition errors ordiscrepancies. Whether or not layer metes and bounds are compared atthis point to reference data, the identification of a pixel bank throughimage analysis (e.g., gradient analysis) can be used to create a mask(731) and used to isolate image data corresponding to contents of apixel well. Such a mask is represented by numeral 825 in FIG. 8D, withthe mask then being applied to the filter the original captured image toproduce filtered data which isolates (733) contents of one or more pixelwells under analysis. The filtering operation then terminates for agiven well in a given image (735).

FIG. 7C is used to discuss processing of filtered data includingoptional boundary processing, introduced above in reference to numerals727 and 729, and the application of a gradient function to identifydiscontinuities within image data representing a deposited layer. Thisprocessing is generally represented by a series of steps, identified byreference numeral 741 in FIG. 7C. As with the other flow chartsdiscussed herein, the method represented in FIG. 7C can once again beperformed by one or more processors acting under the auspices ofinstructions stored on computer-readable media. In connection with theprocessing of FIG. 7A, it is assumed that filtered image data isavailable per-well in order to assess quality of the deposited filmlayer within each given well.

For filtered data, an image characteristic is optionally isolated, perprocess 743, and used to assess quality of the subject layer. This imagecharacteristic can include, without limitation, color intensity, hue,brightness, or other characteristics. In one embodiment, to be furtherdiscussed below, color data is converted to a brightness value accordingto a well-known formula that combines intensities of multiple colorcomponents, e.g., to obtain a grayscale intensity. Other possibilitiesare also possible, such as focusing processing on a given color valueonly, a color difference value, or some other measure. Emphasized datarepresenting the desired characteristic (or the filtered data) is thencompared to stored file data corresponding to the expected pattern forthe layer within the particular print well (e.g., the expected contour),per numerals 745 and 747, in an optional process to detect underfill.For example, as mentioned in reference to FIGS. 8A-8F, the selectedcharacteristic can be compared to outline data from a stored fileindicating the expected contours of the deposited layer. Imageprocessing software can, as part of this comparison, perform contouranalysis to determine whether there is an error, whether that errorrises to the level of a quality or fill defect (749), and whether aspecial remedial measure should be taken in view of any identifieddefect (751). Note that any type of error measure or comparison processcan be used. For example, if there is an error in contour of thedeposited layer, but the error falls below a given criterion or set ofcriteria or an area threshold, the pixel well contours can still befound acceptable. To cite another example, some applications may beagnostic to a deposited layer contour that exceeds the expected wellboundaries, i.e., error can be electively associated with areas ofshortfall only. Many other possible criteria can be used. If contours(e.g., boundaries) of the deposited layer are found to be acceptable,the deposited layer can then be analyzed for gradients within thedeposited layer (i.e., for discontinuities that might represent pixelperformance issues).

As part of this analysis, a gradient function is applied to image data(i.e., the filtered or emphasized data) to identify non-uniformities inthe deposited layer, per numeral 753. In one embodiment, the gradientfilter essentially operates as a high-pass filter, such that lack ofspatial variation results in a zero value for each PEL, while gradients(or discontinuities) result in non-zero attributes (for example, anintensity difference between 8-bit intensity values for adjacent PELs,e.g., also having an 8-bit absolute value, 0-255). This need not be thecase for all embodiments, e.g., it is possible to use processing thatprovides zero or negative value outputs to indicate discontinuities;clearly, many variations exist. The result of gradient analysis isprocessed data comprising a set of values where each value indicateswhether or not a gradient or other discontinuity exists in the dimensionof the selected image characteristic. This is represented by numeral 755in FIG. 7C. Note that nearly any error measure can once again be used todetermine whether an issue such as delamination exists, or whether thereis another substantial issue (757). If such an issue does exist, aresult can be generated (759), once again, affecting further processingor otherwise leading to another form of remedial measure. Once all imagedata for a given pixel well has been processed, the method proceeds tothe next pixel well (755).

FIG. 7D is a flow chart that identifies additional specific processingassociated in one embodiment with gradient analysis and associatedquality analysis. The method of FIG. 7D is generally designated byreference numeral 761. As indicated by numeral 763, filtered image datais optionally simplified as mentioned to emphasize a selectedcharacteristic (e.g. intensity as referenced above); again, any desiredimage attribute can be used, and it is possible also to operate simplyon the raw or filtered image data itself. The data selected forprocessing (G) is extracted as a series of overlapping tiles andconvolved with a gradient operator (represented by the matrix designatorF), per process 765 and function 767; this operation results inprocessed data. In one embodiment, a very simple operator is used totrack very high frequency variation, essentially operating as ahigh-pass filter between adjacent PELs. In this regard, the operator canbe a matrix that convolves each PEL with its immediate neighbor in thefiltered data (or emphasized characteristic of that data, such asintensity), and produces a value dependent on differences. In oneembodiment, the resultant value is zero for continuous image data and anon-zero value if there are differences (discontinuities) with one ormore immediate neighboring PELs. The result of this operation istypically a set of processed data 769 (e.g., result values or gradientvalues for each PEL represented by the filtered data, where eachgradient value both emphasizes the existence of a discontinuity orgradient and quantifies the magnitude of such a discontinuity). Thesevarious gradient values are then processed to detect possible qualityissues associated with a layer in the pixel well under consideration(771). A variety of measures can be used to make this assessment. Forexample, as seen at the right side of FIG. 7D and as represented bynumeral 773, in one embodiment, the absolute values of the gradientvalues can be summed together, with the sum then being compared to athreshold. For example, a sum of sign-less gradient values for theentire well under consideration can be compared to anempirically-determined reference value (e.g., “250000”). If theaggregate gradient magnitudes are smaller than this reference value,then the pixel well can be determined to be healthy (e.g., pass) and ifnot, then the pixel well can be determined to have issues.

Clearly, many other criteria can also be applied. In a second option,represented by box 775 in FIG. 7D, the gradient values can be comparedto one or more statistical measures; for example, as indicated, avariance or standard deviation of the gradient values can be compared toa threshold (e.g., and the pixel well “passed” for the particular layerdeposition if the variance or standard deviation is small relative tothe threshold. Clearly, many examples are possible, as represented bythe box labeled “other criteria” (777) in FIG. 7D, including any form ofstatistical test (779). Note that a combination of measures can also beused; in one specifically-contemplated embodiment, both a sum ofgradient magnitudes and a statistical measure (e.g., variance or std.deviation) for a given pixel well are calculated and compared tothresholds, with the deposited layer “passed” if the sum and thevariance are both small relative to respective thresholds.

Per numeral 781, if the layer is determined to pass for all pixel wellsassociated with a particular layer deposition, then processing of theOLED device substrate can then continue, for example, with the additionof another pixel well layer, a panel encapsulation layer, or some otherprocess; alternatively, if an issue has been detected, processing can beinterrupted, or a remedial measure can be taken in an attempt to fix theidentified issue and to salvage the OLED device substrate in question.

FIGS. 8A-8F are used to further exemplify the various processesdiscussed above.

FIG. 8A represents one hypothetical example of a captured image 801.This image is seen to encompass three pixel wells formed on a substrate803, including part of a first pixel well 805, a second pixel well 807and a third pixel well 809. These various pixel wells can form part ofcircuitry for a single pixel of the OLED device, though this is notrequired. As denoted by numeral 811, each pixel well can have associatedtraces and electronics used to control light generation by lightemissive layers within a corresponding one of the pixel wells. Note thatof the three pixel wells, the middle well 807 is illustrated to have ahypothetical misfill 832 (as a deposited layer does not reach the bankin this area) and a defect within an area of a deposited layer, asreferenced by numeral 835.

FIG. 8B by contrast represents an optional step of trimming image datato focus on one pixel well at a time only, namely, the middle well 807from FIG. 8A. Note that in one embodiment, multiple pixel wells can beanalyzed at the same time (e.g., if those wells have received the samelayer material via a printing process) and in another embodiment, onlyone well is processed at a time. In FIG. 8B, it is seen that image data815 encompasses the well area 807, the bank structure 816 that serves toconfine deposited fluids within the well, and superfluous image data813, for example, representing supporting structures and electronics 811outside of the confines of the bank structure.

As referenced earlier, in one embodiment, captured image data such asrepresented by FIG. 8A or FIG. 8B is filtered to eliminate superfluousimage data. In one embodiment, this filtering is performed usingboundary analysis, such as a second gradient function or otherprocessing that detects bank structure 816. In another embodiment,boundaries of a deposited layer can be identified and compared withreference data (designated by reference numeral 822 in FIG. 8C) and usedto determine whether there is a fill defect. Regardless of specificmethod, these operations can be used to produce a mask that will be usedfor this purpose—the mask is illustrated in FIG. 8D by numeral 825.Image data (e.g., from FIG. 8B) which is matched to hatched region 827will be masked out and not used, and image data which is matched toclear region 829 will not be filtered and will be used in subsequentgradient analysis.

The result of the filtering operation is depicted in FIG. 8E byreference numeral 831. Generally speaking, in this embodiment, thefiltered data includes image data corresponding to the pixel wellconfines (oval 807), null data in other areas (e.g., per numeral 833),and variable image data corresponding to the potential defectsrepresented by numeral 832 and 835. At this point, optional analysis canbe performed to identify a fill issue such as designated by numeral 832.For example, if the method is applied following deposition of a wet ink(e.g., as opposed to a finished, cured layer) it is expected that region832 would have substantially different color characteristics that can beidentified using a color filter, applied in one embodiment. Depending onthe nature of the defect, a remedial measure can be applied in anattempt to remedy the issue. Note that numeral 835, representing apossible defect within the deposited layer, may possess general colorcharacteristics of the deposited layer and so may remain unidentified atthis stage, depending on the nature of processing applied.

FIG. 8F represents processed data 841, generally the result ofapplication of a gradient filter (not shown). In this FIG., valuescorresponding to each PEL (e.g., of FIGS. 8B-8E) are shown as havingnull values except for those values corresponding to discontinuities 842or 845, represented as a gradient contour or nested set of contours;dashed line 843 is superimposed on this FIG. simply to identify pixelwell contours. Note that while layer discontinuity 835 might haveremained undetected prior to the application of the gradient filter, theresult of gradient filter application highlights intensity differencesthat could represent an issue, for example, delamination of one or moreOLED stack layers.

FIGS. 9-10 show respective histograms 901 and 1001 of the absolutevalues of gradients represented by processed data for two hypotheticalpixel wells. Depictions corresponding to the pixel wells are seen at theright-hand side of each FIG., and are respectively numbered 903 and1003. In the respective FIG., numerals 904 and 1004 shows the positionof a pixel confinement bank. Each depiction 903 and 1003 representsgradient values corresponding to PELS representing an image of theassociated pixel well, such that 903 and 1003 represent discontinuitiesin the captured image data associated with the depicted pixel well. FIG.9 represents data for a deposited layer 905 that has no quality issues,whereas FIG. 10 represents data for a deposited layer 1005 that indeedhas quality issues, e.g., a delamination or other defect in the centerof the pixel as represented by a “racetrack effect” 1006 appearing inthe center of the pixel well. Note again that while the depictedgradients are in this example taken from grayscale intensity values, inpractice, a gradient can be applied to many other types of datarepresenting or derived from a captured image.

Referring to the histogram 901 of FIG. 9, as indicated by the FIG., the“y-axis” of the histogram represents the number of occurrences of agiven gradient magnitude, whereas the “x-axis” represents gradientmagnitude. As seen in the upper-right-hand corner of the FIG., in thisexample, the sum of the absolute values of the gradients is “164,391,”and the pixel well depiction 903 is seen to correspond to a layerdeposition with no identifiable quality issues. Note that, as indicatedby the histogram, there is no significant number of occurrences of largegradients, and the histogram has a 3σ gradient value of approximately“21.” Appropriate values for a quality layer in a pixel well can varydepending on layer type (and resultant image appearance), camera type,magnification, lighting, distance from substrate, whether the layer inquestion is wet or dried/cured, pixel well size, and many other factors;one or more thresholds by which to assess layer quality are typicallyempirically determined. In this example, the sum would be compared to athreshold (e.g., “250,000”) determined in association with a specificmanufacturing process, OLED device and machine (with camera). In oneembodiment, a standard is prepared and analyzed in advance usingspecified equipment, for example, using a prepared substrate with goodand bad pixel fills (i.e., with layer defects), or by dynamicallyprinting such a standard with a deliberately-injected pixel defect(i.e., into a reference well) to assess these thresholds. Note also thatas referenced earlier, in one embodiment, multiple quality tests areapplied; this is to say, the gradient sum shown in the upper-right-handcorner of FIG. 9 could theoretically result from a predominance of arelatively small number of very large gradients or a large number ofsmall gradients (the latter in fact being represented by the histogram901 of FIG. 9). Thus, in one embodiment, a statistical measure, in thiscase, 3σ analysis, is also applied to distinguish these cases; asindicated, the 3σ level in the case of FIG. 9 falls at approximately“21,” which indicates that approximately 99% of layer material in thepixel well under analysis produced a gradient value of less than orequal to “21,” a relatively small value. As the standard deviation (a)is dependent on the square of the gradient values and the number of PELsrepresented by the histogram, this value would have been expected to belarger if the gradient sum (164,391) were produced by a greaterincidence of large magnitude gradients. Thus, in this case, the 3σ levelrepresented by the histogram is compared with another threshold (e.g.,“40”), and because pixel data for this pixel well satisfies both tests,the layer analyzed within the pixel well is determined to pass thresholdquality. Note once again that these various tasks are typicallyperformed by software (instructions on computer-readable media) whichcontrol one or more processors to perform these various tasks (includingany desired remedial action). Also, as referenced earlier, many othertypes of criteria and tests can be applied, including withoutlimitation, any type of statistical test, whether based on standarddeviation, a 3σ level, variance, sums, sums of squares, absolute valuesor otherwise. In one embodiment, multiple tests can be applied, forexample, classifying quality of a deposited layer by matching gradientvalues for that layer to one of several ranges of values (distinguishedby one or more thresholds).

FIG. 10 illustrates data for a layer that is determined to have qualityissues. First, note the “racetrack effect” 1006, visible in the gradientvalues, representing possible delamination (e.g., of a cured or finishedfilm following post-printing processing steps to finish the film). Giventhe racetrack effect, it should not be surprising that the depictedhistogram 1003 shows a much more frequent occurrence of large gradientvalues (e.g., representing gradient magnitudes of 30-70), correspondingto the “racetrack” shapes shown within depiction 1003. That is, as notedat the right side of FIG. 10, the sum of (absolute) gradient values inthis case (339,409) is significantly larger than the hypotheticalthreshold of “250,000.” Applying the test references above for FIG. 9, acomparison of these values would indicate that the layer in questionmight have quality issues, and again, remedial measures can optionallybe applied. Note also that the 3σ level in this case, represented byline 1007, is just over “50,” exceeding the hypothetical threshold of“40” used above in connection with FIG. 9.

FIG. 11 shows another embodiment (1100) of a method for processing imagedata. Additional, fewer, or different operations can be performeddepending on the implementation. For example, method 1100 of FIG. 11 canprovide additional functionality beyond the capability to process one ormore images. The order of presentation of the operations of method 1100of FIG. 11 is not intended to be limiting. Thus, although some of theoperational flows are presented in sequence, the various operations canbe performed in various repetitions, concurrently, and in other ordersthan those that are illustrated. Various implementations of the method1100 of FIG. 11 can be in the form of an image processing or printercontrol application having computer-readable instructions that, whenexecuted by one or more processors, cause the processor(s) to performvarious functions depicted in FIG. 11 for analyzing image datarepresenting a pixel well.

Per operation 1102, captured image data is received, for example, frommemory, from a camera or from another destination. In a first example, asingle image can be received at a time, and in another implementation,multiple images can be received, e.g., in connection withmassively-parallel image processing used to provide for very-fastanalysis of the OLED stack films in millions of pixel wells thatcomprise a flat panel display. Accordingly, the captured image datareceived in operation 1102 encompasses at least one pixel wellcontaining an OLED stack film. If the captured image data encompassesmultiple, complete pixel wells, then each can be analyzed from a commonimage, either sequentially, or in parallel. If respective pixel wellshave different layer materials that are to be analyzed, for example,light emissive layers for light of different colors, then analysis ofimage data for these wells can use respective processes, threads,thresholds or other particulars suitable to the layer under analysis. Inone embodiment, image data representing a stream of images can beprocessed in real time, with image processing software tracking wellposition and identifying (in software) which wells have already beenprocessed amongst the captured images and which wells have not (andrespective locations of these wells). The image data can be captured aspart of the printing process of an OLED device substrate or as part of asubsequent quality assurance process. The data also can besimultaneously displayed on a display of a data collection apparatus orof an imaging processing device, or otherwise printed for human-visualinspection.

As another option, the image data can be stored in database of computingdevice, read from a database under control of an automated imageprocessing application of method 1100, and thereby received by the imageprocessing application for processing. A user can optionally select theimage data for reading, for example, using a user interface presentedunder control of an image processing application of method 1100. Asunderstood by a person of skill in the art, the image data captured ofportions of a flat panel display substrate can be stored in a variety offormats. In an illustrative embodiment, the image files can be stored asunprocessed RGB color pixel data.

In operation 1104, filtered image data representing an OLED stack underanalysis can be isolated from a captured image that encompassessuperfluous data (i.e., outside of a pixel well). Numerous differentalgorithms can be used for this purpose (i.e., extracting datacorresponding to an OLED film or stack from a captured image. As alludedto previously, in one embodiment, a gradient filter (i.e., a secondgradient operator) can be used to identify periphery of a layer underanalysis and, in so doing, extract data just corresponding to thedeposited layer. It is also possible simply to use a templatecorresponding to pixel well geography, and to use that template to maskthe image under analysis. In yet another embodiment, operation 1104 iscombined with the image capture step (i.e., such that image data iscaptured only for the layer in question, or is otherwise filtered at thecamera or prior to receipt by the image processing system to reduce theamount of superfluous data).

In operation 1106, filtered data, for example, representing an emissivematerial layer (“EML”) of an OLED stack can be converted to a formatthat emphasizes a particular image characteristic. In this case, colorimage data representing the deposited layer is converted to grayscaleintensity data according to the well-known formula I(Grayscale)=Red*0.3+Green*0.59+Blue*0.11. In various formula referencedbelow, the grayscale intensity values will be generally represented asG_(x,y), where x and y refer to PEL position within the captured image.Note that this type of emphasis is purely optional, i.e., it is possibleto perform gradient analysis on color image data directly, and it isalso possible to capture monochromatic image data; clearly, otheralternatives also exist.

Regarding operation 1108, the filtered data representing a pixel well(emphasized or otherwise) is then convolved with a gradient filter togenerate processed data representing gradients within the depositedlayer. In one embodiment, the gradient filter is expressed as a relativesimple matrix and the convolution is applied using a window within PELsof the filtered image data; this is expressed mathematically as:

${\begin{bmatrix}G_{11} & G_{12} & G_{13} \\G_{21} & G_{22} & G_{23} \\G_{31} & G_{32} & G_{33}\end{bmatrix} \ominus \left\lbrack {F_{1}\mspace{14mu} F_{2}} \right\rbrack} = \begin{bmatrix}\left( {{G_{11}*F_{1}} + {G_{12}*F_{2}}} \right) & \left( {{G_{12}*F_{1}} + {G_{13}*F_{2}}} \right) \\\left( {{G_{21}*F_{1}} + {G_{22}*F_{2}}} \right) & \left( {{G_{22}*F_{1}} + {G_{23}*F_{2}}} \right) \\\left( {{G_{31}*F_{1}} + {G_{32}*F_{2}}} \right) & \left( {{G_{23}*F_{1}} + {G_{33}*F_{2}}} \right)\end{bmatrix}$

-   -   where:        -   G represents the grayscale intensity value of a PEL;        -   Θ is the convolution operator;        -   [F₁ F₂] is gradient filter mask; and        -   the matrix on the right is the resultant processed data

The depicted convolution operation produces gradient values thatemphasize vertical line differences between horizontally-adjacent PELsof captured image data representing the layer under scrutiny. Aperfectly uniform layer would have a filtered image matrix of allzeroes. In this regard, a sum of the absolute values of all the gradientvalues represented by the processed data can present a criterion forevaluating the uniformity of the layer under scrutiny, and hence theOLED stack.

Regarding the selection of a gradient filter mask, there are manydifferent types of masks that can be used. Gradient filters aredistinguishable based on the mask applied. The exemplary mask indicatedabove presents a dual symmetry along a horizontal axis. Other types ofgradient filters can also be used. For example, in a Sobel mask, ahorizontal axis divides the top and bottom into identical [1, 0, −1]while a vertical axis divides the mask into [1, 2, 1] and [−1, −2, −1].Some exemplary gradient filter masks that can be used are shown in thetable below:

Sobel Masks $\quad\begin{bmatrix}1 & 0 & {- 1} \\2 & 0 & {- 2} \\1 & 0 & {- 1}\end{bmatrix}$ $\quad\begin{bmatrix}1 & 2 & 1 \\0 & 0 & 0 \\{- 1} & {- 2} & {- 1}\end{bmatrix}$ Prewitt Masks $\quad\begin{bmatrix}1 & 0 & {- 1} \\1 & 0 & {- 1} \\1 & 0 & {- 1}\end{bmatrix}$ $\quad\begin{bmatrix}1 & 1 & 1 \\0 & 0 & 0 \\{- 1} & {- 1} & {- 1}\end{bmatrix}$ Kirsh Masks $\quad\begin{bmatrix}5 & {- 3} & {- 3} \\5 & 0 & {- 3} \\5 & {- 3} & {- 3}\end{bmatrix}$ $\quad\begin{bmatrix}5 & 5 & 5 \\{- 3} & 0 & {- 3} \\{- 3} & {- 3} & {- 3}\end{bmatrix}$

Note that these gradient filters emphasize both vertical and horizontaldiscontinuities in the imaged layer.

In operation 1110, the method proceeds to analyze quality of thedeposited film under scrutiny. Once again, the method can be entirelyautomated, e.g., under the control of software. Summing the absolutevalues of the brightness differences can create a score for theevaluation of uniformity of a layer within a given pixel well, andtherefore quality of the deposited OLED stack within a given pixel well.In this example, a very low gradient sum correlates to a uniform,delamination-free pixel.

However, as previously alluded to, a gradient sum score alone may not bedispositive on the issue of film quality. For example, a film layerhaving a large discontinuity in the film center, while being otherwiseuniform could have an equal gradient sum to a layer having uniformlysmall gradients distributed throughout the film.

Therefore, in one embodiment, a statistical measure such as a variance,standard deviation, histogram, or other measure of matrix elements iscomputed and used to effectively distinguish a film layer having animage containing significant intensity changes, which can indicate adefective film, from a film that contains fairly uniform small intensitychanges, which can indicate a satisfactory film.

By evaluating both the gradient or processed data (e.g., the sum of theprocessed gradient values) and data representing the distribution ofgradients values throughout the pixel well, the quality of an OLED stackfilm can be determined. In various embodiments, the data generated fromthe filtered intensity data, can be compared to thresholds. Factors thatcan impact the quality of an OLED stack film image, such as differentlighting, different positioning of a camera, and different substrateconstruction and inks used to form the pixel can once again have aneffect on what constitutes a good gradient sum and good histogramcharacteristics. Accordingly, calibration using a standard can be usedto define good gradient sum and statistical characteristics under aspecified set of conditions, and a threshold gradient sum and athreshold statistical reference can be determined by using images offilm layers of defined uniformity. Using simple statistical qualitycontrol, a mean and standard deviation for acceptable pixel images canbe determined. Depending on the level of quality desired, a failurethreshold (or set of thresholds) can be set a corresponding number ofstandard deviations beyond the mean identified for good pixel values.

In operation 1112, a determination can be made concerning whether or nota layer in the pixel well at an identified pixel cell location isdefective in some manner. For example, the determination can be made ifthe sum and variance do not satisfy the thresholds defined for asatisfactory layer. If the determination can be that the layer isdefective in some manner, processing continues in operation 1114. If thedetermination can be that the layer at the identified pixel welllocation is not defective, processing continues in operation 1116.

In operation 1114, a layer within a pixel well can be identified as atleast potentially defective. For example, the particular pixel celland/or well can be flagged for visual inspection by an inspector toconfirm the determination. A notification to an inspector or operatorcan be provided to the inspector using a speaker, a printer, a displayor by preparing and sending a message via text, email, or voicemail, orby saving information related to the determination in a database forsubsequent review. Other forms of remedial measure are also possible, asdiscussed previously. In one embodiment, a result that a quality issueexists affects subsequent processing, for example, interrupting furtherimage consideration and/or subsequent OLED device fabrication processes.In another embodiment, processing continues via operation 1116, with aninspector or operator being notified of the existence of any potentialdefect, with such potential defect addressed at a later point in time.

In operation 1116, a determination can be made concerning whether or notthere is more image data to process. If the determination can be thatthere is more image data to process, processing continues in operation1100. If the determination can be that there is no more image data toprocess, the method 1100 can cease. Statistics can be calculatedrelative to the number of defective pixels identified for the entiretyof the OLED device to determine whether processing should continue orthe OLED device should be rejected, or whether other remedial actionsshould be taken.

An alternative embodiment of systems and methods is depicted in the flowdiagram shown in FIG. 12. In method 1200, the steps of isolating(filtering) image data for a film layer to focus on just layerinformation within a pixel well can be performed after steps ofconverting captured image data to emphasized data, for example,representing grayscale intensity, and applying a gradient function. Asbefore, captured image data is first received, per step 1202. This datais then converted to grayscale intensity data per numeral 1204, toemphasize this selected image characteristic. Per step 1206, a gradientfilter is then applied, to emphasize boundaries and edges of a depositedlayer, OLED confinement bank, and other features appearing in thecaptured image data. With respect to steps 1208 and 1210, once theprocessed (gradient) data has been generated, inverted data can beoptionally created, so as to help isolate contours of the interior of apixel well. As an example, a target image or mask can be created foreach pixel well to serve as a mask to mask out gradient valuescorresponding to superfluous PELs (i.e., representing structures otherthan the film layer to be analyzed). As an example, following gradientanalysis, the pixel well confinement bank can be represented in white(gradient, non-zero value) while other structures including the pixelwell are represented in black. This data is then inverted and processed,to assist with forming a mask where values outside of the pixel well arerepresented as black (zero values), and the interior of the pixel wellis represented as white (binary value “1”). This mask can then beapplied back to the gradient values so as to isolate gradient values forthe layer of interest, per step 1212.

More specifically, a target white image can be created to define a pixelwell region, having white values (“1”) in areas corresponding to thepixel well and black values (“0”) corresponding to other areas of thetarget image. An inverted version of the processed data (gradientvalues) can then be converted to binary and combined with the targetimage according to the equation Σ_(i=j) ^(W) ^(T) Σ_(j=1) ^(I) ^(T)=I_(i+a,j+b)&T_(ij), where I is the inverted gradient matrix and T isthe target matrix (mask). This equation essentially positions the maskover the inverted gradient matrix (i.e., the processed data) andproduces a maximum result for relative positioning between the two wherethe mask and pixel well location are exactly aligned. That is, becausethe operation above applied a Boolean “and” operation, and because themask has entries that are either white (“1”) or black (“0”), the “and”operation will produce a maximum where the two are precisely aligned.The proper position of the mask is ascertained, and then used to maskwith the processed (gradient) data in order to isolate gradient valuescorresponding just to the area of the pixel well.

In the remaining steps 1216-1224, once gradient values for the filmlayer in question have been isolated, the data is then processed asdescribed earlier, to ascertain layer quality (relative to the existenceof defects), and to provide notifications and/or remedial action aspreviously discussed.

Note that various embodiments of the foregoing exist. For example, thegradient function can be applied at any stage of the process to imagedata, whether that image data has been filtered or converted or not.Also, while a relatively simple example of a horizontal gradientoperation was mentioned in connection with the equations above, morecomplex gradient functions can be applied, for example, that do not relyon matrix math, that compute vertical gradients, or that computegradients over PELs other than a nearest neighbor.

In yet another embodiment, image analysis focuses on deposition regionsthat do not necessarily comprise pixel wells. For example, in a systemthat deposits a layer of film over a broad region, imaging can be usedfor one or more discrete target areas making up the region to assessfilm quality on a parceled basis. Such an implementation generallyfunctions as described above, e.g., with an image being captured foreach target area or parcel under analysis, and with a gradient functionapplied to this image data (or to a derivative thereof, filtered,emphasized or otherwise), to develop processed data. This data can thenbe analyzed to ascertain the existence of possible defects within thegiven target area. Other variations also exist.

Methods were described above that can be embodied in a computer-readablemedium (e.g., as software or firmware), or otherwise in a machine, forexample, a network machine, a printer, a manufacturing system for OLEDdevices, or in another form.

While the principles of this invention have been described in connectionwith specific embodiments, it should be understood clearly that thesedescriptions are made only by way of example and are not intended tolimit the scope of the invention. What has been disclosed herein hasbeen provided for the purposes of illustration and description. It isnot intended to be exhaustive or to limit what is disclosed to theprecise forms described. Many modifications and variations will beapparent to the practitioner skilled in the art. What is disclosed waschosen and described in order to best explain the principles andpractical application of the disclosed embodiments of the art described,thereby enabling others skilled in the art to understand the variousembodiments and various modifications that are suited to the particularuse contemplated. It is intended that the scope of what is disclosed bedefined by the following claims and their equivalence.

What is claimed is:
 1. A computer-implemented method for monitoringquality of a film deposited on a substrate, the computer-implementedmethod comprising: capturing a digital image of a structure of the film;processing the digital image to form image data of the digital image;processing the image data to identify any gradients that satisfy a firstgradient threshold; identifying a quality defect if any gradients whichsatisfy the first threshold also satisfy a second gradient threshold;and identifying that the film is not defective with respect to thestructure if no gradients satisfy the second gradient threshold.
 2. Thecomputer-implemented method of claim 1, wherein the structure is aportion of the surface of the film.
 3. The computer-implemented methodof claim 1, wherein the structure is inside the film.
 4. Thecomputer-implemented method of claim 1, wherein the first gradientthreshold is a non-zero threshold.
 5. The computer-implemented method ofclaim 4, wherein processing the image data comprises applying a filterto the image data to form filtered image data, and applying an operatorto the filtered image data to emphasize intensity variation representedin the image data.
 6. The computer-implemented method of claim 5,wherein processing the image data further comprises obtaining thegradients using the operator and comparing an absolute value of amagnitude of each gradient to the first threshold.
 7. Thecomputer-implemented method of claim 6, wherein the operator is a Sobeloperator.
 8. The computer-implemented method of claim 6, wherein thesubstrate is a display substrate, and the structure is a pixel of thedisplay substrate.
 9. The computer-implemented method of claim 5,wherein the filter comprises a boundary detection function.
 10. Thecomputer-implemented method of claim 9, wherein applying the filtercomprises using the boundary detection function to form a mask, andusing the mask to isolate image data corresponding to the structure. 11.A computer-implemented method for monitoring quality of a film depositedon a substrate, the computer-implemented method comprising: capturing adigital image of an electro-optical structure of the film; processingthe digital image to form image data of the digital image; processingthe image data to identify any gradients that satisfy a first gradientthreshold; identifying a quality defect if any gradients which satisfythe first threshold also satisfy a second gradient threshold; andidentifying that the film is not defective with respect to the structureif no gradients satisfy the second gradient threshold.
 12. Thecomputer-implemented method of claim 11, wherein the structure islocated at the surface of the film.
 13. The computer-implemented methodof claim 11, wherein the structure is located inside the film.
 14. Thecomputer-implemented method of claim 11, wherein processing the imagedata comprises applying a filter to the image data to form filteredimage data, applying an operator to the filtered image data to emphasizeintensity variation represented in the image data, obtaining thegradients using the operator, and comparing an absolute value of amagnitude of each gradient to the first gradient threshold.
 15. Thecomputer-implemented method of claim 14, wherein the operator is a Sobeloperator.
 16. The computer-implemented method of claim 14, wherein thesubstrate is a display substrate, and the structure is a pixel of thedisplay substrate.
 17. The computer-implemented method of claim 14,wherein the filter comprises a boundary detection function, and applyingthe filter comprises using the boundary detection function to form amask, and using the mask to isolate image data corresponding to thestructure.
 18. The computer-implemented method of claim 11, whereinprocessing the image data comprises applying a filter to the image datato form filtered image data, applying an operator to the filtered imagedata to emphasize intensity variation represented in the image data,obtaining the gradients using the operator, summing the gradients, andcomparing the sum to the first gradient threshold or the second gradientthreshold.
 19. A computer-implemented method for monitoring quality of afilm deposited on a display substrate, the computer-implemented methodcomprising: capturing a digital image of an portion of the filmcorresponding to a pixel of the display substrate; processing thedigital image to form image data of the digital image; processing theimage data to identify any gradients that satisfy a first gradientthreshold; identifying a quality defect if any gradients which satisfythe first threshold also satisfy a second gradient threshold; andidentifying that the film is not defective with respect to the structureif no gradients satisfy the second gradient threshold.
 20. Thecomputer-implemented method of claim 19, wherein processing the imagedata comprises applying a filter to the image data to form filteredimage data, applying an operator to the filtered image data to emphasizeintensity variation represented in the image data, and obtaining thegradients using the operator.